| 2,210 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
8.8257742e-05 |
| 3,487 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
7.263041e-05 |
| 3,563 |
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift |
2023 |
SIGMOD |
7.2042148e-05 |
| 4,202 |
Kepler: Robust Learning for Faster Parametric Query Optimization |
2023 |
SIGMOD |
6.7374091e-05 |
| 4,258 |
LEON: A New Framework for ML-Aided Query Optimization |
2023 |
VLDB |
6.6994722e-05 |
| 4,683 |
AutoSteer: Learned Query Optimization for Any SQL Database |
2023 |
VLDB |
6.4716143e-05 |
| 5,214 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
6.2248104e-05 |
| 5,241 |
FASTgres: Making Learned Query Optimizer Hinting Effective |
2023 |
VLDB |
6.2154384e-05 |
| 5,456 |
Eraser: Eliminating Performance Regression on Learned Query Optimizer |
2024 |
VLDB |
6.1239873e-05 |
| 5,649 |
Sample-Efficient Cardinality Estimation Using Geometric Deep Learning |
2024 |
VLDB |
6.052326e-05 |
| 5,683 |
How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks |
2025 |
SIGMOD |
6.0392183e-05 |
| 5,788 |
Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries |
2023 |
SIGMOD |
5.9947442e-05 |
| 5,871 |
PilotScope: Steering Databases with Machine Learning Drivers |
2024 |
VLDB |
5.9639223e-05 |
| 6,308 |
Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective |
2024 |
VLDB |
5.8177833e-05 |
| 6,586 |
Can Large Language Models Be Query Optimizer for Relational Databases? |
2026 |
SIGMOD |
5.7430662e-05 |
| 6,632 |
Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges |
2023 |
VLDB |
5.7270153e-05 |
| 6,660 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
5.7178404e-05 |
| 6,710 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
5.7019157e-05 |
| 7,033 |
Rethinking Learned Cost Models: Why Start from Scratch? |
2023 |
SIGMOD |
5.6168499e-05 |
| 7,070 |
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model |
2025 |
VLDB |
5.6080535e-05 |
| 7,460 |
T3: Accurate and Fast Performance Prediction for Relational Database Systems With Compiled Decision Trees |
2025 |
SIGMOD |
5.5215755e-05 |
| 7,931 |
SlabCity: Whole-Query Optimization using Program Synthesis |
2023 |
VLDB |
5.4238328e-05 |
| 7,977 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
5.4142519e-05 |
| 8,332 |
Learned Offline Query Planning via Bayesian Optimization |
2025 |
SIGMOD |
5.3528188e-05 |
| 9,133 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
5.2229655e-05 |
| 9,276 |
BASE: Bridging the Gap between Cost and Latency for Query Optimization |
2023 |
VLDB |
5.204289e-05 |
| 9,300 |
GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints |
2026 |
SIGMOD |
5.1987909e-05 |
| 9,546 |
Athena: An Effective Learning-based Framework for Query Optimizer Performance Improvement |
2025 |
SIGMOD |
5.1604755e-05 |
| 9,610 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
5.1526493e-05 |
| 9,656 |
BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach |
2023 |
SIGMOD |
5.1453267e-05 |
| 9,670 |
Low Rank Learning for Offline Query Optimization |
2025 |
SIGMOD |
5.1452097e-05 |
| 9,720 |
APQO: An Adaptive Framework for Parametric Query Optimization |
2026 |
SIGMOD |
5.1349531e-05 |
| 9,797 |
NeuSO: Neural Optimizer for Subgraph Queries |
2026 |
SIGMOD |
5.1257999e-05 |
| 9,893 |
Approximate Sketches |
2024 |
SIGMOD |
5.1134687e-05 |
| 9,954 |
Conformal Prediction for Verifiable Learned Query Optimization |
2025 |
VLDB |
5.1038322e-05 |
| 9,956 |
Graph Transformers for Query Plan Representation: Potentials and Challenges |
2025 |
VLDB |
5.1038322e-05 |
| 10,148 |
Improving DBMS Scheduling Decisions with Accurate Performance Prediction on Concurrent Queries |
2025 |
VLDB |
5.0715586e-05 |
| 10,285 |
Towards Full Stack Adaptivity in Permissioned Blockchains |
2024 |
VLDB |
5.0448662e-05 |
| 10,310 |
veDB-HTAP: a Highly Integrated, Efficient and Adaptive HTAP System |
2025 |
VLDB |
5.0386264e-05 |
| 10,336 |
An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL |
2025 |
SIGMOD |
5.0200193e-05 |
| 10,412 |
Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis] |
2026 |
SIGMOD |
4.9793485e-05 |
| 10,484 |
NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis] |
2026 |
SIGMOD |
4.9793485e-05 |
| 10,489 |
On Self-Designing Learned Indexes |
2026 |
SIGMOD |
4.9793485e-05 |
| 10,508 |
Succinct Structure Representations for Efficient Query Optimization |
2026 |
SIGMOD |
4.9793485e-05 |
| 10,532 |
Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads |
2026 |
SIGMOD |
4.9793485e-05 |
| 10,596 |
SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer |
2026 |
SIGMOD |
4.9793485e-05 |
| 10,633 |
Divo: Learning a Stable and Effective Query Optimizer with a Diverse Workload |
2026 |
SIGMOD |
4.9793485e-05 |
| 10,693 |
Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking |
2026 |
SIGMOD |
4.9793485e-05 |
| 10,698 |
LIO: A lightweight and interpretable query optimizer based on an evolutionary forest |
2026 |
VLDB |
4.9793485e-05 |
| 10,700 |
Sample-based Distinct Cardinality Estimation for Multiple Attributes in Multi-Dataset Queries |
2026 |
VLDB |
4.9793485e-05 |